OpenAI has introduced GPT-5.2, describing it as part of a new model series aimed at professional knowledge work. The model was trained and deployed using NVIDIA hardware, including NVIDIA Hopper systems and GB200 NVL72 infrastructure. This release highlights a broader trend in which major AI developers rely on NVIDIA’s full-stack platforms to train and scale large models.
The development of advanced AI systems depends heavily on three approaches: pretraining, post-training, and test-time scaling. While newer reasoning models focus on using computation during inference to process complex tasks, pretraining and post-training remain core stages for improving model capability. These processes require extensive computational resources, often involving tens or hundreds of thousands of GPUs operating together.
NVIDIA reports that its GB200 NVL72 systems provide notable training speed improvements over its earlier Hopper architecture, based on results from MLPerf Training benchmarks. The GB300 NVL72 further increases training speed, contributing to shorter development cycles for model creators.
A significant portion of current large language models across different modalities continue to be trained on NVIDIA platforms. This reflects ongoing dependence within the AI ecosystem on scalable, high-performance compute and networking systems to support the training and deployment of increasingly complex models.
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